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- Title
SMS Spam Detection Using Multiple Linear Regression and Extreme Learning Machines.
- Authors
Ali, Zuhair Hussein; Salman, Hayder Mahmood; Harif, Alaa Hassan
- Abstract
With the growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper, presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI benchmark dataset was used. The proposed model produced recall, precision, F-measure, and accuracy values of 98.7%, 93.3%, 95.9%, and 98.2%, respectively.
- Subjects
MACHINE learning; SPAM email; TEXT messages; CELL phones; HAM
- Publication
Iraqi Journal of Science, 2023, Vol 64, Issue 10, p5442
- ISSN
0067-2904
- Publication type
Article
- DOI
10.24996/ijs.2023.64.10.45